Nutrition Support for Persistent Inflammation, Immunosuppression, and Catabolism Syndrome
Bibliographic record
Abstract
Despite tremendous advances in critical care, multiple-organ failure continues to be a significant problem. However, in recent years, far fewer patients with multiple-organ failure die early, but many experience ongoing immune dysregulation and are developing persistent inflammation, immunosuppression, and catabolism syndrome (PICS). Most PICS patients are discharged to nonhome destinations, fail to rehabilitate, and succumb to indolent death. From a nutrition perspective, patients with PICS experience persistent inflammation-induced cachexia despite evidenced-based recommended intensive care unit nutrition support. Recent basic and translational research indicates that prolonged expansion of myeloid-derived suppressor cells plays a central role in the pathogenesis of PICS. Myeloid-derived suppressor cells express arginase 1, which depletes arginine, causing immunosuppression and impaired wound healing. This is the rationale for arginine supplementation in PICS. Other nutrition support recommendations for PICS are based on inferences made from other patient populations who experience similar persistent inflammation-induced cachexia. These include patients with established cancers, major burns, and sarcopenia. These patients experience anabolic resistance, but studies show that this can be overcome by providing higher levels of protein and certain specific amino acids. Nutrition support guidelines recommend provision of >1.5 g/kg/d of protein and indicate that higher levels may be needed. Protein composition is also important. There is good evidence that leucine can promote anabolism in patients with cancer and sarcopenia. Finally, anabolic interventions-including intensive insulin, oxandrolone, propranolol, and resistance exercise-have proven to be effective in patients with major burns and are likely relevant in combating PICS cachexia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".